mamba-2.8b-chat-no_robots

MAMBA (2.8B) 🐍 fine-tuned on H4/no_robots dataset for chat / instruction

Model Card is still WIP!

<div style="text-align:center;width:250px;height:250px;"> <img src="https://huggingface.co/clibrain/mamba-2.8b-chat-no_robots/resolve/main/mamba_no_robos-logo.png" alt="mamba-no_robots logo""> </div>

Base model info

Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.

Dataset info

Look Ma, an instruction dataset that wasn't generated by GPTs!

Dataset Description

• Repository: https://github.com/huggingface/alignment-handbook
• Paper:
• Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
• Point of Contact: Lewis Tunstall

Dataset Summary

No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn instructions across the following categories:

Usage

pip install torch==2.1.0 transformers==4.35.0 causal-conv1d==1.0.0 mamba-ssm==1.0.1

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

CHAT_TEMPLATE_ID = "HuggingFaceH4/zephyr-7b-beta"

device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_name = "clibrain/mamba-2.8b-chat-no_robots"

eos_token = "<|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.eos_token = eos_token
tokenizer.pad_token = tokenizer.eos_token
tokenizer.chat_template = AutoTokenizer.from_pretrained(CHAT_TEMPLATE_ID).chat_template

model = MambaLMHeadModel.from_pretrained(
        model_name, device=device, dtype=torch.float16)

messages = []
prompt = "Tell me 5 sites to visit in Spain"
messages.append(dict(role="user", content=prompt))

input_ids = tokenizer.apply_chat_template(
            messages, return_tensors="pt", add_generation_prompt=True
).to(device)

out = model.generate(
    input_ids=input_ids,
    max_length=2000,
    temperature=0.9,
    top_p=0.7,
    eos_token_id=tokenizer.eos_token_id,
)

decoded = tokenizer.batch_decode(out)
assistant_message = (
    decoded[0].split("<|assistant|>\n")[-1].replace(eos_token, "")
)

print(assistant_message)

Gradio Demo

git clone https://github.com/mrm8488/mamba-chat.git
cd mamba-chat

pip install -r requirements.txt
pip install -q gradio==4.8.0

python app.py \
--model clibrain/mamba-2.8b-chat-no_robots \
--share

Evaluations

Coming soon!

Acknowledgments

Thanks to mamba-chat for heavily inspiring our work